Stanislav Fort

I am currently away from Stanford as a Google AI Resident.

Before that, I was a PhD student at Stanford University. My research spans physics, AI, and deep learning. I am excited about applications of artificial intelligence and machine learning in physics, emergent phenomena, and the role of complexity in physical systems.

I completed my Bachelors and Masters (Part III of the Tripos) at Trinity College, University of Cambridge, and another Masters at Stanford University.

I worked at Institute of Astronomy on galaxy clusters in X-ray, Albert Einstein Institute on large scale data mining for pulsar discovery, Perimeter Institute for Theoretical Physics on perturbative approaches to black hole formation in AdS-like geometries, and DAMTP on cross-correlations of gamma-rays and the CMB in the sky. At Stanford, I have worked on quantum gravity, theoretical neuroscience, computer vision, cosmology, and astrophysics.

I actively co-organize and coach at the Czech Astronomy Olympiad, setting problems and preparing students for the IOAA. I sometimes lecture at the Czech Physics Olympiad and prepare students for IPhO. I co-organized the 1st and 2nd International Workshop on Astronomy and Astrophysics in Estonia and the Czech Republic. I am also an amateur astrophotographer.

On top of my research, I work on a number of side projects in mathematics, physics, and CS. They usually involve coding in Python, NumPy, and TensorFlow.

Twitter  /  GitHub  /  LinkedIn


I'm interested in physics, emergence, and AI. My current focus is on applying deep learning methods to physical sciences, and deep learning theory.

7. Stiffness: A New Perspective on Generalization in Neural Networks
Stanislav Fort, PaweĊ‚ Krzysztof Nowak, Srini Narayanan

Understanding how resistant to deformations, or stiff, neural network loss surfaces are, how this property relates to generalization, and the effect learning rate has on it.

6. Adaptive Quantum State Tomography with Neural Networks
Stanislav Fort (equal contributions), Yihui Quek (equal contributions), Hui Khoon Ng

Learning to learn about quantum states using neural networks, swarm optimization and particle filters. We develop a new algorithm for quantum state tomography that learns to perform the state reconstruction directly from data and achieves orders of magnitude computational speedup while retaining state-of-the-art reconstruction accuracy.

A subset accepted at the 4th Seefeld Workshop on Quantum Information and 22nd Annual Conference on Quantum Information Processing (QIP 2019) as a poster.

5. The Goldilocks zone: Towards better understanding of neural network loss landscapes
Stanislav Fort, Adam Scherlis

A connection between optimization on random low-dimensional hypersurfaces and local convexity in the neural network loss landscape.

Accepted for publication at AAAI 2019 in Hawaii as an oral presentation and a poster.

A subset accepted at the Modern Trends in Nonconvex Optimization for Machine Learning workshop at ICML 2018 and BayLearn 2018 as The Goldilocks zone: Empirical exploration of the structure of the neural network loss landscapes (link here). Accepted as an oral presentation at the Theoretical Physics for Machine Learning Aspen winter conference.

4. The ATHENA WFI science products module
David N Burrows, Steven Allen, Marshall Bautz, Esra Bulbul, Julia Erdley, Abraham D Falcone, Stanislav Fort, Catherine E Grant, Sven Herrmann, Jamie Kennea, Robert Klar, Ralph Kraft, Adam Mantz, Eric D Miller, Paul Nulsen, Steve Persyn, Pragati Pradhan, Dan Wilkins

A paper on the proposed Athena X-ray observatory's WFI science products module. My part involved exploring the use of AI techniques on board.

Published at the Proceedings Volume 10699, Space Telescopes and Instrumentation 2018: Ultraviolet to Gamma Ray.

3. Towards understanding feedback from supermassive black holes using convolutional neural networks
Stanislav Fort

A novel approach to detection of X-ray cavities in clusters of galaxies using convolutional neural architectures.

Accepted at the Deep Learning for Physical Sciences workshop at NIPS 2017.

2. Gaussian Prototypical Networks for Few-Shot Learning on Omniglot
Stanislav Fort

An architecture capable of dealing with uncertainties for few-shot learning on the Omniglot dataset.

Accepted and presented at BayLearn 2017.
Accepted at the Bayesian Deep Learning workshop at NIPS 2017.

Essential code available on GitHub.

1. Discovery of Gamma-ray Pulsations from the Transitional Redback PSR J1227-4853
T. J. Johnson, P. S. Ray, J. Roy, C. C. Cheung, A. K. Harding, H. J. Pletsch, S. Fort, F. Camilo, J. Deneva, B. Bhattacharyya, B. W. Stappers, M. Kerr

A pulsar detection in gamma-ray.

Class projects

At Stanford, I worked on the following class projects:

Fun side projects

I work on a number of side projects and fun problems in mathematics, physics, and CS. Some of them are shown here.

Drawing an envelope/barn without lifting one's pen - all 88 (44 unique and their mirrors) solutions at once.

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